
"""simple docstring"""


import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
      from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor



class 	__a	:






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Union[str, Any]=13							,   UpperCAmelCase_			:				Optional[Any]=30							,   UpperCAmelCase_			:				Tuple=2							,   UpperCAmelCase_			:				Optional[Any]=3							,   UpperCAmelCase_			:				List[str]=True							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				Dict=32							,   UpperCAmelCase_			:				Dict=5							,   UpperCAmelCase_			:				Any=4							,   UpperCAmelCase_			:				Union[str, Any]=37							,   UpperCAmelCase_			:				Optional[int]="gelu"							,   UpperCAmelCase_			:				Optional[Any]=0.1							,   UpperCAmelCase_			:				Optional[Any]=0.1							,   UpperCAmelCase_			:				List[str]=10							,   UpperCAmelCase_			:				List[str]=0.02							,   UpperCAmelCase_			:				Optional[int]=None							,   UpperCAmelCase_			:				Dict=2							,   )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		image_size
              UpperCamelCase       			=		patch_size
              UpperCamelCase       			=		num_channels
              UpperCamelCase       			=		is_training
              UpperCamelCase       			=		use_labels
              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		type_sequence_label_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		scope
              UpperCamelCase       			=		encoder_stride

              # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
              UpperCamelCase       			=		(image_size // patch_size) ** 2
              UpperCamelCase       			=		num_patches + 1






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

              UpperCamelCase       			=		None
              if self.use_labels:
                     UpperCamelCase       			=		ids_tensor([self.batch_size]							,   self.type_sequence_label_size      )

              UpperCamelCase       			=		self.get_config()

              return config, pixel_values, labels






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Any:







              """simple docstring"""
              return ViTConfig(
                  image_size=self.image_size							,   patch_size=self.patch_size							,   num_channels=self.num_channels							,   hidden_size=self.hidden_size							,   num_hidden_layers=self.num_hidden_layers							,   num_attention_heads=self.num_attention_heads							,   intermediate_size=self.intermediate_size							,   hidden_act=self.hidden_act							,   hidden_dropout_prob=self.hidden_dropout_prob							,   attention_probs_dropout_prob=self.attention_probs_dropout_prob							,   is_decoder=UpperCAmelCase_							,   initializer_range=self.initializer_range							,   encoder_stride=self.encoder_stride							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				Tuple      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		ViTModel(config=UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				int      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		ViTForMaskedImageModeling(config=UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(
                  result.reconstruction.shape							,   (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

              # test greyscale images
              UpperCamelCase       			=		1
              UpperCamelCase       			=		ViTForMaskedImageModeling(UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()

              UpperCamelCase       			=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.reconstruction.shape							,   (self.batch_size, 1, self.image_size, self.image_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.type_sequence_label_size
              UpperCamelCase       			=		ViTForImageClassification(UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_							,   labels=UpperCAmelCase_      )
              self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.type_sequence_label_size)      )

              # test greyscale images
              UpperCamelCase       			=		1
              UpperCamelCase       			=		ViTForImageClassification(UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()

              UpperCamelCase       			=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.type_sequence_label_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.prepare_config_and_inputs()
              (
                  (
                  UpperCamelCase
              )			,							(
                  UpperCamelCase
              )			,							(
                  UpperCamelCase
              )			,							
              )       			=		config_and_inputs
              UpperCamelCase       			=		{"pixel_values": pixel_values}
              return config, inputs_dict





@require_torch
class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Tuple				=      (
           (
               ViTModel,
               ViTForImageClassification,
               ViTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase_		:       Dict				=      (
           {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
           if is_torch_available()
           else {}
       )
       UpperCamelCase_		:       Union[str, Any]				=      True

       UpperCamelCase_		:       Any				=      False
       UpperCamelCase_		:       List[str]				=      False
       UpperCamelCase_		:       Any				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		ViTModelTester(self      )
              UpperCamelCase       			=		ConfigTester(self							,   config_class=UpperCAmelCase_							,   has_text_modality=UpperCAmelCase_							,   hidden_size=37      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Any:







              """simple docstring"""
              self.config_tester.run_common_tests()






       @unittest.skip(reason="ViT does not use inputs_embeds"      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Any:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase       			=		model_class(UpperCAmelCase_      )
                     self.assertIsInstance(model.get_input_embeddings()							,   (nn.Module)      )
                     UpperCamelCase       			=		model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(UpperCAmelCase_							,   nn.Linear      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase       			=		model_class(UpperCAmelCase_      )
                     UpperCamelCase       			=		inspect.signature(model.forward      )
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     UpperCamelCase       			=		[*signature.parameters.keys()]

                     UpperCamelCase       			=		["pixel_values"]
                     self.assertListEqual(arg_names[:1]							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[Any]:







              """simple docstring"""
              for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase       			=		ViTModel.from_pretrained(UpperCAmelCase_      )
                     self.assertIsNotNone(UpperCAmelCase_      )





def 						lowerCamelCase__      (				)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
       return image



@require_torch
@require_vision
class 	__a	(   unittest.TestCase						):






       @cached_property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	int:







              """simple docstring"""
              return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"      ) if is_vision_available() else None






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"      ).to(UpperCAmelCase_      )

              UpperCamelCase       			=		self.default_image_processor
              UpperCamelCase       			=		prepare_img()
              UpperCamelCase       			=		image_processor(images=UpperCAmelCase_							,   return_tensors="pt"      ).to(UpperCAmelCase_      )

              # forward pass
              with torch.no_grad():
                     UpperCamelCase       			=		model(**UpperCAmelCase_      )

              # verify the logits
              UpperCamelCase       			=		torch.Size((1, 1_000)      )
              self.assertEqual(outputs.logits.shape							,   UpperCAmelCase_      )

              UpperCamelCase       			=		torch.tensor([-0.2744, 0.8215, -0.0836]      ).to(UpperCAmelCase_      )

              self.assertTrue(torch.allclose(outputs.logits[0, :3]							,   UpperCAmelCase_							,   atol=1e-4      )      )






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Dict:







              """simple docstring"""
              # ViT models have an `interpolate_pos_encoding` argument in their forward method,
              # allowing to interpolate the pre-trained position embeddings in order to use
              # the model on higher resolutions. The DINO model by Facebook AI leverages this
              # to visualize self-attention on higher resolution images.
              UpperCamelCase       			=		ViTModel.from_pretrained("facebook/dino-vits8"      ).to(UpperCAmelCase_      )

              UpperCamelCase       			=		ViTImageProcessor.from_pretrained("facebook/dino-vits8"							,   size=480      )
              UpperCamelCase       			=		prepare_img()
              UpperCamelCase       			=		image_processor(images=UpperCAmelCase_							,   return_tensors="pt"      )
              UpperCamelCase       			=		inputs.pixel_values.to(UpperCAmelCase_      )

              # forward pass
              with torch.no_grad():
                     UpperCamelCase       			=		model(UpperCAmelCase_							,   interpolate_pos_encoding=UpperCAmelCase_      )

              # verify the logits
              UpperCamelCase       			=		torch.Size((1, 3_601, 384)      )
              self.assertEqual(outputs.last_hidden_state.shape							,   UpperCAmelCase_      )

              UpperCamelCase       			=		torch.tensor(
                  [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]      ).to(UpperCAmelCase_      )

              self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,   UpperCAmelCase_							,   atol=1e-4      )      )






       @slow
       @require_accelerate
       @require_torch_gpu
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		ViTModel.from_pretrained("facebook/dino-vits8"							,   torch_dtype=torch.floataa							,   device_map="auto"      )
              UpperCamelCase       			=		self.default_image_processor

              UpperCamelCase       			=		prepare_img()
              UpperCamelCase       			=		image_processor(images=UpperCAmelCase_							,   return_tensors="pt"      )
              UpperCamelCase       			=		inputs.pixel_values.to(UpperCAmelCase_      )

              # forward pass to make sure inference works in fp16
              with torch.no_grad():
                     UpperCamelCase       			=		model(UpperCAmelCase_      )







"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





